本文整理汇总了Python中weka.classifiers.Evaluation.pctIncorrect方法的典型用法代码示例。如果您正苦于以下问题：Python Evaluation.pctIncorrect方法的具体用法？Python Evaluation.pctIncorrect怎么用？Python Evaluation.pctIncorrect使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.Evaluation的用法示例。
在下文中一共展示了Evaluation.pctIncorrect方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import pctIncorrect [as 别名]
logfile = "logs/" + classifiername + "_" + dataname + crossvalidate + ".log"
log=open(logfile, 'w', bufsize) # open general log file
for num in range(int(p['j48.initial']),fulltrainset.numInstances(),(fulltrainset.numInstances() / int(p['j48.numdatapoints']))):
filelimit.write(str(num))
trainset = Instances(fulltrainset,0,num) # create training set
trainset.setClassIndex(trainset.numAttributes() - 1)
log.write("---------------------------------\nTraining Set Size: " + str(trainset.numInstances()) + ", Test Set Size: " + str(testset.numInstances()) + ", Full data set size: " + str(fulltrainset.numInstances()) + "\n")
for dataset in [testset, fulltrainset]:
algo = J48()
algo.buildClassifier(trainset)
algo.setConfidenceFactor(float(p['j48.C']))
evaluation = Evaluation(trainset)
output = PlainText() # plain text output for predictions
output.setHeader(trainset)
buffer = StringBuffer() # buffer to use
output.setBuffer(buffer)
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
x = time.time()
if (int(crossvalidate)):
evaluation.crossValidateModel(algo, dataset, 10, rand, [output, attRange, outputDistribution])
else:
evaluation.evaluateModel(algo, dataset, [output, attRange, outputDistribution])
log.write("Time to evaluate model: " + str(time.time() - x) + "\n")
log.write(evaluation.toSummaryString())
filelimit.write("," + str(evaluation.pctIncorrect()))
filelimit.write("\n")
filelimit.close()
log.close()